Research in the field of automated detection of eye orientation and eye gaze direction (referred to herein as “automated gaze detection”) has led to a variety of applications. Examples are laser eye surgery, weapon system targeting, and computer accessibility for the handicapped.
A common characteristic of conventional gaze detection systems is that a single camera is focused on a single eye. Many systems require a fixed or severely restricted distance between the camera and the eye. Because of this constraint, such systems are unsuitable for situations where head movement is likely, such as in virtual reality simulators. The fixed distance constraint is also limiting in desktop computing environments.
Previous efforts to accommodate head movement have resulted in solutions that use electronic or mechanical devices to dynamically measure the camera-to-eye distance. Some sort of head gear is typically required for these systems. For this reason, the usefulness of these systems is limited.
Most current gaze tracking research is based on a two-step approach, using a fixed camera viewing the face of the user to first find the eyes in a wide field-of-view image, and to then determine the direction of gaze using high resolution images of the eyes.
One method of quickly and accurately finding the eyes in an image is based on the “red-eye” phenomenon, well known to users of flash cameras. Two sets of infrared illuminators are used, with one set spaced apart from the other. Two images (obtained in rapid succession) are subtracted, leaving the “red-eye” reflections from the pupils as well as other noise and reflection points. With additional filtering and rule-based classification, the eyes can be located.
After the eyes are located in an image, the second step is to determine the direction of gaze. Various methods have been reported, including the use of contact lenses and artificial neural networks. Other methods are based on models of the eye and the shape of the iris and eyelid.